A hyperspectral lidar system (HSL) dedicated to underwater detection is studied. Using a white light laser combined with a tunable filter as a light source, the integrated detection of ranging information and spectral information of underwater targets is realized. The system has the echo detection capability of 160 channels in 450nm-610nm wavelength, and the spectral resolution is 10nm. Substance differentiation was achieved by testing different underwater targets, including sponges, ferromanganese crusts, titanomagnetite, and apatite. The underwater application of hyperspectral lidar is initially explored.
Grassland plays an important role in regional economic development and terrestrial ecosystem security. Remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area.
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